A federated reinforcement learning framework for balancing rapidity and availability in deep space networks

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Task scheduling improved in deep space networks, balancing responsiveness with availability.
Framework models the scheduling process as a Markov decision process to enhance efficiency.
Analysis uses a high-fidelity deep space network simulator informed by actual orbital data.
Supports the integration of automation and local autonomy in future deep space missions.

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Novel framework improves task scheduling and resource allocation in deep space, indicating enhanced automation and local autonomy.

A 2025 study studied this question.

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